Machine learning models for text and image processing
Document Type
Book Chapter
Publication Title
Machine Learning Models and Architectures for Biomedical Signal Processing
First Page
153
Last Page
177
Publisher
Elsevier
School
School of Engineering
RAS ID
77602
Abstract
The advent of Machine Learning (ML) techniques has rapidly improved the healthcare system over the past decade. Many health applications including biomedical signal processing require text and image processing. As per the concern for the performance of health-related applications, it is still a challenge. In today’s technological era, early-stage disease detection and relevant disease diagnosis have become urgent needs. ML models are tremendously useful in the preprocessing and implementation of text and image processing for better results. This chapter discusses the various preprocessing and performance-improving methods involved in text and image processing. The proposed work is configured to work for both text data processing and image processing. As per the concern for text-based inputs, the proposed model uses a sentiment analysis approach for data processing. Here, a hybrid model such as Recurrent Neural Network with Long-Short Term Memory is proposed for high accuracy in text data processing. Apart from this, for image processing, a novel Convolutional Neural Network approach is applied for brain tumor classification using Magnetic Resonance images. The proposed approach results in improved accuracy when compared to other architectures tested.
DOI
10.1016/B978-0-443-22158-3.00007-7
Access Rights
subscription content
Comments
Soewu, T., Kaur, H., Sandhu, R., Sandhu, P., Ghai, D., Dhir, K., & Tripathi, S. L. (2025). Machine learning models for text and image processing. In Machine Learning Models and Architectures for Biomedical Signal Processing (pp. 153-177). Academic Press. https://doi.org/10.1016/B978-0-443-22158-3.00007-7